An Adaptive Neuro-Fuzzy Inference System for Forecasting Australia’s Domestic Low Cost Carrier Passenger Demand

Srisaeng, Panarat; Baxter, Glenn; Wild, Graham

doi:10.3846/16487788.2015.1104806

Cited by 30 publications

(35 citation statements)

References 59 publications

(116 reference statements)

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“…An accurate estimation affects a firm's economic decisions and plans for the future (Srisaeng, Baxter, & Wild, 2015). This is relevant for sports clubs either since estimation demand of games is vital for sports clubs.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Attendance Demand of Sports Games

Şahin¹

2018

Journal of International Scientific Researches

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Demand information is a major tool for decision makers and sports administrators. It is also required by dynamic ticket pricing models of sports events, which have been an increasingly popular research topic. It can be inferred that the accuracy of the demand forecasting plays a crucial role on critical decisions. Considering this point, soft computing techniques, artificial neural networks and adaptive neuro-fuzzy inference system (ANFIS), are utilized to forecast attendance demand of sports games in this study. Classical regression models have been used in almost all of the studies conducted for the same purpose. It is aimed to propose competitive alternative forecasting approaches. To do so, multiple models are developed to forecast attendance demand rate of sports events. To generalize the use of the proposed approaches, as a general term, the demand rate is considered. By multiplying the demand rate by the total number of tickets offered for sale, the number of spectators (attendance demand) is obtained. The proposed approaches can be used for almost all sports disciplines by making some modifications. Demand factors of indoor and outdoor sports games may differ. Therefore, the determinants of demand are selected accordingly. The real data of the sports clubs are used to evaluate the forecasting results of the models. The mean absolute percentage error (MAPE) is used for that purpose. As a result, the MAPE values of the proposed models are below %10 that means the proposed models can be utilized for demand forecasting purposes. The proposed models may be utilized in other industries as well.

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Section: Introductionmentioning

confidence: 99%

Forecasting Attendance Demand of Sports Games

Şahin¹

2018

Journal of International Scientific Researches

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“…Compared to the ANN, the ANFIS model is more transparent to the user and causes less memorization errors. Consequently, several advantages of the ANFIS exist, including its adaptation capability, nonlinear ability, and rapid learning capacity [32]. This approach is essentially a rule-based fuzzy logic model whose rules are developed during the training process of the model [52].…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

“…The ANFIS has some advantages, including the ability to capture the nonlinear structure of a process, adaptation capability, and rapid learning capacity. The ANFIS has been applied to many areas including economics [31], passenger demand forecasting [32], energy [33], and the environment [34]. However, it has been underused in sports, having only been used for simulated soccer agents [35].…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games

Şahin

Erol

2017

MCA

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Abstract:The main purpose of this study was to develop and apply a neural network (NN) approach and an adaptive neuro-fuzzy inference system (ANFIS) model for forecasting the attendance rates at soccer games. The models were designed based on the characteristics of the problem. Past real data was used. Training data was used for training the models, and the testing data was used for evaluating the performance of the forecasting models. The obtained forecasting results were compared to the actual data and to each other. To evaluate the performance of the models, two statistical indicators, Mean Absolute Deviation (MAD) and mean absolute percent error (MAPE), were used. Based on the results, the proposed neural network approach and the ANFIS model were shown to be effective in forecasting attendance at soccer games. The neural network approach performed better than the ANFIS model. The main contribution of this study is to introduce two effective techniques for estimating attendance at sports games. This is the first attempt to use an ANFIS model for that purpose.

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“…Aside from the aforementioned works, other methods have been used for image segmentation, such as incorporating adaptive local information into fuzzy clustering [12], arbitrary noise models via solution of minimal surface problems [13], clustering technique optimized by cuckoo search [14], modified Gaussian mixture models incorporating local spatial information [15], conditional random field learning with convolutional neural network features [16], dynamic incorporation of wavelet filter in FCM [17,18], proliferation index evaluation [19], and fuzzy active contour model with kernel metric .…”

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confidence: 99%